FIBO Operational Ontologies Briefing for the Object Management Group March 20, 2013, Reston, VA David Newman Strategic Planning Manager, Senior Vice President, Enterprise Architecture Chair, Semantic Technology Program, EDM Council
Wells Fargo Disclaimer The content in this presentation represents only the views of the presenter and does not represent or imply acknowledged adoption by Wells Fargo Bank. Examples used within are purely hypothetical and are used for illustrative purposes only and are not intended to reflect Wells Fargo policy or intellectual property. March 20, 2013 2
2008 Global Financial Crisis Stimulated Need for Improved Financial Data Standards Financial industry needs better data standards: to ensure there is high data consistency, promote data comparability and facilitate transparency as a prerequisite for effective institutional and macroprudential risk analysis and reporting Financial data standards are needed for: identification of legal entities, their jurisdictions and control ownership hierarchies identification of financial contracts and instruments classification and data linkage for aggregation One of the most significant lessons learned from the global financial crisis that began in 2007 was that banks information technology (IT) and data architectures were inadequate to support the broad management of financial risks. Many banks lacked the ability to aggregate risk exposures and concentrations quickly and accurately at the bank group level, across business lines and between legal entities. Principles for effective risk data aggregation and risk reporting Basel Committee on Banking Supervision, June 2012 March 20, 2013 3
Regulatory Requirements for Risk Data Aggregation and Reporting Per FSB and Basel, global SIFIs must comply with risk data aggregation requirements by early 2016. IT infrastructure and architecture need to support risk data aggregation capabilities in normal and during stress times. A bank should establish integrated data taxonomies and architecture across the banking group, which includes information on the characteristics of the data (metadata), as well as use of single identifiers and/or unified naming conventions for data including legal entities, counterparties, customers and accounts. A bank should be able to capture and aggregate all material risk data across the banking group. Data should be available by business line, legal entity, asset type, industry, region and other groupings that permit identifying and reporting risk exposures, concentrations and emerging risks. Risk reports should be accurate in times of stress and (largely) automated to minimize errors Risk data must be complete and captured/aggregated across the enterprise Risk data must be accurate and the firm must be able to reconcile/validate reports March 20, 2013 4
Some Operational Benefits of FIBO Ensures that all instruments truly conform to the standard definition of the contract. Displays meaning of contract to both humans and machines Flags instruments that are exceptions or do not align Data rollups and aggregations are based upon the reliability of knowing that the underlying data has been validated for conformity Provides a rich flexible multi-tiered taxonomy An instrument can be classified across multiple facets Data can be rolled up and aggregated at multiple levels of the taxonomy Data is linked in a network graph structure that allows for high data diversity and variability of relationships Efficient querying and identification of complex relationships Agile adding and linking of new and changing data relationships Graph structures lend themselves well to visual representations of data Metadata annotations are integrated into FIBO and provide standard definitions and useful links to related information March 20, 2013 5
Visualization of FIBO Derivatives Taxonomy (3 levels shown of multi-level taxonomy) FIBO has a Rich Multi-Tiered Taxonomy that can be used to Classify and Aggregate Data at Many Levels and Across Many Facets * Screen shots from Gruff query and visualization tool from Franz Inc. March 20, 2013 6
Legal Entity Ownership and Control Relationships can be Queried and Displayed Semantic web enables data visualizations which are more holistic and descriptive than basic columnar views Legend FIBO aligns with LEI March 20, 2013 7
FIBO Describes Business Entities, Legal Jurisdictions and Country Locations Data relationships are shown as links within a network graph Legend March 20, 2013 8
March 20, 2013 9 Counterparty Trades, FIBO Classifications, ISDA UPI Codes FIBO provides an automatic mapping between FIBO named product classes and identifiers and ISDA named product identifiers (or any other linked product taxonomies)
Credit Default Swap Transactions (top) and Netting Transactions (bottom) Tabular view of credit default swap transactions FIBO can identify netting transactions (a CDS protection buyer becomes a CDS protection seller for the same reference entity but also creates a potential scenario for cascading risk) March 20, 2013 10
FIBO Identifies Instrument Contractual Terms and Attributes Holistic view of a credit default swap, and the data types and classifications of many key attributes March 20, 2013 11
FIBO Flags Instruments that Do Not Conform to the Standard Contractual Definitions in FIBO Reference Entity is missing Example: Reference Entity, a necessary CDS attribute, is missing from this particular CDS Instrument is flagged by FIBO because it cannot be further classified as a specialization of a Credit Default Swap i.e. (Index CDS or Single Name CDS) indicating misalignment Instruments that are conformant are fully classified at the most fine grained levels of the FIBO taxonomy and are deemed to be fully aligned with the contractual specification thus enhancing data reliability March 20, 2013 12
FIBO Identifies Ultimate Parents, their Descendents and Trading Counterparties This capability allows for the rollup of both positions and exposures of the subsidiaries to the level of the ultimate parent for risk analysis March 20, 2013 13
March 20, 2013 14 FIBO Identifies International Trading Partners via Country of Business Entity Legal Jurisdiction FIBO defines meaningful relationships between entities
Rollups of Total Positions by Country FIBO provides the ability to rollup positions for legal entities within jurisdictions that are domiciled within countries for any desired level of a product taxonomy March 20, 2013 15
Business Entities and Aggregation by FIBO Swap Contract Classes FIBO allows rollups of a business entity s total position across trades for particular types of instruments and product classifications March 20, 2013 16
Business Entities and Higher Level Aggregation by FIBO Swap Contract Classes In this example, FIBO displays rollups for a business entity at the level of all derivatives contracts March 20, 2013 17
Rollup of Ultimate Parents and their Descendents Total Positions for Instruments FIBO supports the ability to rollup the total positions of an ultimate parent and it s subsidiaries to show aggregate exposure for particular instruments March 20, 2013 18
Rollup of Ultimate Parents and their Descendents Total Swap Positions using FIBO Classes FIBO supports the ability to rollup the total positions of an ultimate parent and it s subsidiaries to show aggregate exposure at increasingly higher levels of the taxonomy March 20, 2013 19
Visualization of Transitive Exposures Across Counterparties Visually demonstrates that Wall Street Bank has more direct and indirect exposures to counterparties than London Bank or other Banks. March 20, 2013 20
Visualization of Transitive Exposures across Counterparties with Higher Positions Visualization of trading relationships across counterparties provides a more intuitive and cognitively clear view of transitive exposures However when filtered by a threshold value for a higher total amount exposure, London Bank actually exhibits greater risk than Wall Street Bank, since it has a greater number of exposures that exceed the threshold value. March 20, 2013 21
Visualization of Ownership Hierarchies and Exposures to Counterparties Solid blue lines represent ownership and control relations. Violet lines represent exposures due to trading Legend March 20, 2013 22
FIBO Metadata Annotations Provide Useful Descriptions and Links to Related Information Annotations represents supplemental descriptive information that can be linked to the elements within FIBO and can be directly queried. Example of some annotations for Interest Rate Swap Contract and related CFTC information. Provenance, policy, regulations, security constraints, etc. can also be linked. Definitions for all types of Swap Contracts are retrieved. Annotations eliminate need to define metadata using a separate independent product or repository. March 20, 2013 23
Legacy Data can be Processed Semantically Without Requiring Conversion or Migration Legacy data can be collected from diverse sources, mapped, integrated with FIBO, classified (based upon alignment with business concepts) and then aggregated for query and reporting purposes Excel interface Excel Adapter Excel to Ontology Mappings Integrated data Spread sheets Mapping between legacy data and ontologies is the only prerequisite Semantic Information Integration Platform semantic query Risk Analyst Relational Relational Adapter Adapters SQL to Ontology Mappings Ontology to FIBO Mappings Ontologies Credit Default Swap Table Interest Rate Swap Table Legacy Relational Databases Interest Rate Swap Map Credit Default Swap Map FIBO Swap Contract Ontology Semantic Triple Store Semantic vendors provide tools for data federation and integration March 20, 2013 24
Proposed FIBO Architecture for Institutional and Macroprudential Oversight Financial Institutions Legacy Database(s) Informational Sharing across Regulatory Agencies Legacy Database(s) Regulatory Agencies Semantic Network Graph Analysis Trading Trading & Compliance System System(s) Mapping Institutional Risk Analyst Mapping Semantic Information Integration Platform Swap Trade & Compliance Reporting Mapping FpML Swap Data Repository Database(s) Legal Entity Data Provider(s) Mapping Semantic Triple Store Mapping Semantic Information Integration Platform Regulatory Risk Analyst Semantic Triple Store Ontologies Semantically defined financial data standards ensures data quality and fidelity between institutions and regulators Ontologies Trusted Financial Linked Data Cloud March 20, 2013 25